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F.A.Z.-Column by Emanuel Derman : Little Big Data

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Kepler’s laws described the patterns of the planets, but not their causes. Newton found a cause; he showed that Kepler’s laws were a mathematical consequence of Newton’s own theories of gravitation (the inverse square law of attraction) and motion (Force = mass times acceleration). 

How did Newton discover his theories? For sure, the orbiting planets and falling apples didn’t announce the laws that drove them. Wrote John Maynard Keynes about Newton: I fancy his pre-eminence is due to his muscles of intuition being the strongest and most enduring with which a man has ever been gifted. Keynes understood something about the discovery of truth which many of his more formal economist disciples have never learned.

Useful, picturesque, but not entirely true

Theories are descriptions of the laws of the world; they can be right, partially right or totally wrong. What all theories have in common is that,  like God’s voice to Moses in the desert, they proclaim: I am what I am. Theories stand on their own feet.

Newton’s laws have been supplanted by Einstein’s, but that doesn’t mean that Newton is an approximation to Einstein. Newton is to Einstein as cursive is to typing, or as navigation by the stars is to the Global Positioning System. Two different approaches reach the same end by different means, with different accuracies. One doesn’t approximate the other. Both are theories that describe facts. 

The final mode of understanding is a model. A model compare something we don’t understand to something we do. So, for example, the famous liquid drop model of the atomic nucles pretends that the nucleus is a drop of water that can vibrate and rotate and even fission into two. Useful, picturesque, but not entirely true. Similarly, the Black-Scholes financial option model compares the uncertain movement of stock prices to the diffusion of smoke from a cigarette tip. Useful, up to a point -- but not fact. Models are metaphors, graven images of reality but not reality itself, analogies whose incautious use can unleash all the dangers of idolatry that God warned against in the second of his commandments.

Against the bewitchment

There’s one final mode of understanding: statistics, the statistical analysis that lies behind Big Data. Statistics seeks to find past tendencies and correlations in data, and assumes they will persist. But, in a famous unattributed phrase, correlation does not imply causation.

Big Data is useful, but is not a replacement for the classic ways of understanding the world. Data has no voice. There is no “raw” data. Choosing what data to collect takes insight; making good sense of it requires the classic methods: you still need a model, a theory, or intuition to find a cause.

“Philosophy is a battle against the bewitchment of our intelligence by means of language,” wrote Wittgenstein. I take that to mean that language can deceive our natural intuition, and we need philosophy to reclaim it.

In a similar sense, I would argue, science is a battle against the bewitchment of our intelligence by data.

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